We propose a hierarchical entity graph convolutional network (HEGCN) for multi-hop relation extraction. We encode the documents in a document chain using a bi-directional long short-term memory (BiLSTM) layer [1]}. On top of the BiLSTM layer, we use two graph convolutional networks (GCN), one after another in a hierarchy. In the first level of the GCN hierarchy, we construct a separate entity mention graph on each document of the chain using all the entities mentioned in that document. Each mention of an entity in a document is considered as a separate node in the graph. We use a graph convolutional network (GCN) to represent the entity mention graph of each document to capture the relations among the entity mentions in the document. We then construct a unified entity-level graph across all the documents in the chain. Each node of this entity-level graph represents a unique entity in the document chain. Each common entity between two documents in the chain is represented by a single node in the graph. We use a GCN to represent this entity-level graph to capture the relations among the entities across the documents. We concatenate the representations of the nodes of the subject entity and object entity and pass it to a feed-forward layer with softmax for relation classification.
